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Stimulus-Specific Adaptation and Deviance Detection inthe Rat Auditory CortexNevo Taaseh1, Amit Yaron1, Israel Nelken1,2*

1 Department of Neurobiology, Silberman Institute of Life Sciences, Hebrew University, Jerusalem, Israel, 2 The Edmond and Lily Safra Center for Brain Sciences and

Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel

Abstract

Stimulus-specific adaptation (SSA) is the specific decrease in the response to a frequent (‘standard’) stimulus, which doesnot generalize, or generalizes only partially, to another, rare stimulus (‘deviant’). Stimulus-specific adaptation could resultsimply from the depression of the responses to the standard. Alternatively, there may be an increase in the responses to thedeviant stimulus due to the violation of expectations set by the standard, indicating the presence of true deviancedetection. We studied SSA in the auditory cortex of halothane-anesthetized rats, recording local field potentials and multi-unit activity. We tested the responses to pure tones of one frequency when embedded in sequences that differed from eachother in the frequency and probability of the tones composing them. The responses to tones of the same frequency werelarger when deviant than when standard, even with inter-stimulus time intervals of almost 2 seconds. Thus, SSA is presentand strong in rat auditory cortex. SSA was present even when the frequency difference between deviants and standards wasas small as 10%, substantially smaller than the typical width of cortical tuning curves, revealing hyper-resolution infrequency. Strong responses were evoked also by a rare tone presented by itself, and by rare tones presented as part of asequence of many widely spaced frequencies. On the other hand, when presented within a sequence of narrowly spacedfrequencies, the responses to a tone, even when rare, were smaller. A model of SSA that included only adaptation of theresponses in narrow frequency channels predicted responses to the deviants that were substantially smaller than theobserved ones. Thus, the response to a deviant is at least partially due to the change it represents relative to the regularityset by the standard tone, indicating the presence of true deviance detection in rat auditory cortex.

Citation: Taaseh N, Yaron A, Nelken I (2011) Stimulus-Specific Adaptation and Deviance Detection in the Rat Auditory Cortex. PLoS ONE 6(8): e23369.doi:10.1371/journal.pone.0023369

Editor: Izumi Sugihara, Tokyo Medical and Dental University, Japan

Received February 24, 2011; Accepted July 15, 2011; Published August 10, 2011

Copyright: � 2011 Taaseh et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This study was supported by an EU FP6 grant (NOVELTUNE consortium, http://cordis.europa.eu/fp6), by grants from the Israel Science Foundation (219/05 and 72/09) to I.N. (http://www.isf.org.il/), and by a grant from the Gatsby Charitable Foundation to the Interdisciplinary Center for Neural Computation at theHebrew University (http://www.gatsby.org.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of themanuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

The auditory system processes a continuously changing auditory

scene. The detection of violations to regularities in the sound

stream may be critical for survival, and may play an important role

in the formation of auditory objects (e.g. [1,2]). Neural

mechanisms of deviance detection have been extensively investi-

gated, mainly using the oddball paradigm [3,4]. In this paradigm,

deviance is typically produced by presenting a rare (‘deviant’)

stimulus against a background of a frequent (‘standard’) stimulus.

In cat auditory cortex [5,6], rat inferior colliculus [7], barn owl

midbrain and forebrain [8], mouse and rat auditory thalamus

[9,10] and rat auditory cortex [11,12], the responses of neurons to

a tone are larger when that tone is deviant than when it is

standard. This effect, called stimulus-specific adaptation (SSA),

depends on the physical difference between the standard and the

deviant stimuli, on the probability of appearance of the deviant

tone, and on the inter-stimulus time interval [5,6,7]. SSA shares

many properties with, (but is probably not identical to, [12,13])

mismatch negativity (MMN), a component of the auditory event-

related potentials (ERPs) that is elicited by deviant tones in

humans [14].

Current research is somewhat ambiguous regarding the

definition of SSA. The term itself emphasizes the adaptation of

the responses to the standard tone. However, the hallmark of SSA

is the large response to the deviant. This large response could be

due to the fact that the deviant is rare and, therefore, the response

it evokes is not adapted. But the deviant also represents a violation

of the expectation to hear a standard. Indeed, the MMN literature

emphasizes the responses to the deviant tone, and a substantial

effort has been made to demonstrate that MMN is not (or not

only) due to the rarity of the deviant, but is at least partially due to

the violation of the regularity of the tone sequence caused by the

presentation of the deviant [15,16,17].

Two types of sound sequence have been used to uncover the

factors contributing to the responses to deviants. The first is the

‘deviant-alone’ control, in which the tone sequence consists of

mainly silent trials, broken by the occasional deviant tone at the

same probability as in the oddball paradigm (but otherwise in

random positions) [15]. In these sequences, there is presumably no

regularity to break, and therefore the response should reflect rarity

alone, in contrast with the response to deviants with standards,

that includes a potential contribution of deviance as well.

However, the response to the deviant in the deviant-alone

PLoS ONE | www.plosone.org 1 August 2011 | Volume 6 | Issue 8 | e23369

sequences may be large simply because the auditory system is

stimulated overall at a much slower rate than in the oddball

sequence. This problem is alleviated, but not fully solved, by the

‘deviant among many standards’ control [16,17], which keeps the

rate of deviant tone presentations low but replaces the standard

tone presentations with a number of different stimuli, each with a

low probability of appearance, thus canceling the special status of

the deviant. Responses to the deviant in this case presumably

reflect its rarity while controlling for the overall activation of the

auditory system, and therefore serve as an appropriate baseline for

establishing the presence of a component which is sensitive to

deviance in the responses to deviants in the oddball sequences.

Studies of human ERPs using both types of control sequences

strongly suggest that the MMN is indeed an index of true deviance

detection. However, the assumptions underlying both types of

control sequences are not transferable in a simple way from the

study of gross potentials such as MMN to neuronal responses in

auditory cortex. In this study, we examined deviance detection in

auditory cortex of rats. To do so, we used a variety of stimulus

conditions, including the control conditions used in human

experiments. These data made it possible to disentangle the effects

of tone rarity and the effects of tone deviance by using a simple

model that fitted well the responses. The main result of the paper

is the demonstration of a response component that is due

specifically to deviance detection in rat auditory cortex.

Results

We presented sound stimuli to the right ear of halothane

anaesthetized rats and recorded local field potentials (LFP) and

extracellular spiking activity from left auditory cortex using

tungsten electrodes. Based on the responses to pure tones spanning

the range of 1–64 kHz, two frequencies evoking large responses

were selected for further study. The lower frequency was denoted

f1, the higher was denoted f2, and they were selected such that the

frequency difference between them, defined as Df = (f22f1)/f1,

was 10%, 21%, 44% or 96%. These values correspond to 0.141,

0.275, 0.526 and 0.971 of an octave, respectively. We presented f1

and f2 in oddball sequences (as in [6]), but we also embedded them

in several control sequences that have not been used in previous

studies of SSA in animals. For the sake of clarity, we first discuss

the responses to the oddball sequences at a single Df, then

introduce the control sequences, and finally discuss the effects of Df

and inter-stimulus interval on the responses. In order to reduce

ambiguity, the word ‘frequency’ will refer in this paper only to the

acoustic frequency of the pure tones. Other meanings of

‘frequency’ will be designated by ‘rate’ (of stimulus presentation)

or ‘probability of occurrence’ (of a tone of a given frequency within

a test sequence).

Stimulus-specific adaptationThe oddball sequences consisted of 500 pure tone beeps (30 ms

duration, 5 ms rise/fall time), presented at an inter-stimulus

interval (ISI) of 300 ms unless explicitly stated otherwise. Two

oddball sequences were used. In one sequence, 95% of the stimuli,

at random, had frequency f1, and the other 5% had frequency f2,

so that f1 was frequent (‘standard’) while f2 was rare (‘deviant’).

This sequence is called ‘Deviant f2’. To determine whether the

difference in the responses to the standard and to the deviant tones

was due to their different probability of presentation or to the

frequency difference between them, we used another sequence in

which the roles of the two frequencies were switched, so that f1

was deviant and appeared in 5% of the trials, while f2 was the

standard. This sequence is called ‘Deviant f1’. For comparison, we

presented also a third sequence in which the two frequencies

appeared with equal probability (half of the tone presentations

each, randomly).

Figure 1A describes schematically the two oddball sequences, as

well as the equal-probability sequence. Figures 1B and 1C display

the average LFP as well as the multiunit activity (MUA) recorded

simultaneously from a typical recording site (f1 = 13.3 kHz,

f2 = 19.2 kHz, corresponding to Df of 44%, 70 dB SPL). In the

‘Deviant f1’ sequence, the LFP response to f1 (the deviant) was

considerably larger than the response to f2 (the standard). On the

other hand, in the ‘Deviant f2’ sequence, the response to f1 (now

the standard) was smaller than the response to f2. The LFP

responses to the two frequencies in the equal-probability sequence

were comparable, and matched the corresponding responses to the

same tones when standard, but were substantially smaller than the

responses to the same tones when deviant. Thus, the LFP

responses were similarly depressed by tones with probabilities of

occurrence of 50% and 95%, but this depression did not

generalize to other tones with probability of occurrence of 5%,

at least for Df = 44%.

In order to quantify the effect of presentation probability on

tone response we used the contrast between the responses to the

same frequency when it was standard and when it was deviant,

called the ‘stimulus-specific adaptation index’ or SI [5]:

SI1~d(f1){s(f1)

d(f1)zs(f1); SI2~

d(f2){s(f2)

d(f2)zs(f2)

where d(fi) and s(fi) represent the peak responses to frequency fi

when it was deviant and standard, respectively (see Methods for

details of the measurement of peak responses for LFP and

multiunit activity). For the LFP responses in Fig. 1B, SI1 was 0.27,

and SI2 was 0.35. Both contrasts were positive, demonstrating the

appreciable effect of stimulus probability on the sensory responses.

The MUA responses measured at the same site are displayed in

Fig. 1C. Similarly to the LFP, the MUA response to each of the

tones was smaller when standard than when deviant. Remarkably,

while in the equal-probability sequence the MUA response evoked

by f2 was substantially weaker than that evoked by f1, in the

Deviant f2 sequence the MUA evoked by the deviant f2 was

actually larger than that evoked by the standard f1, the opposite of

what one would predict from the frequency selectivity of this site.

Figure 2A shows the frequency-specific contrasts between the

standard and the deviant responses, SI1 and SI2, for all the

recording sites tested with Df = 44%. As shown in Ulanovsky et al.

[5], if the change in response size were due to a general decrease in

the excitability of the neural signal (‘fatigue’), data would have

SI1+SI2 = 0, corresponding to points along the reverse diagonal.

In fact, in virtually all cases not only SI1+SI2.0 held, but also

both SI1 and SI2 were individually positive. Thus, the example in

Fig. 1C is typical.

The common contrast between the deviant and standard

responses was used to characterize the average effect of adaptation

for this specific pair of frequencies [6,10]:

CSI~d(f1)zd(f2){s(f1){s(f2)

d(f1)zd(f2)zs(f1)zs(f2)

For the responses in Fig. 1, CSI = 0.31. The distribution of the

common contrast CSI for all the data with Df = 44% is shown in

Deviance Detection in Rat Auditory Cortex


Fig. 2B. The CSI was essentially always positive, demonstrating

the robustness of the SSA in rat auditory cortex.

Controls for SSAThe main goal of this study was to elucidate the effects of the

standard tone on the deviant responses. We therefore used several

control sequences in which the two tested tones had the same

probability of occurrence as the deviant in the oddball sequences,

while the other tone presentations in the sequence, the ‘context’,

had different frequency compositions. Figure 3A describes the

control sequences.

The first control consisted of tones of 20 different frequencies,

including f1 and f2. Each frequency was presented with an equal

probability of 5% in a pseudo-random order, so f1 and f2 were as

rare as the deviants in the oddball sequences, but so were all the

other frequencies. The 20 frequencies were equally distributed on

a logarithmic scale that spanned about twice Df, with f1 and f2

symmetrically positioned (the 5th and 16th frequencies among the

20). Thus, the frequencies were densely packed in a relatively

narrow frequency band. For Df = 44% the frequency ratio

between adjacent frequencies was 3.37%. This sequence will be

referred to here as the narrowband diverse sequence, or ‘Diverse-

narrow’ in short.

The second control was similar to the diverse-narrow sequence,

but the frequency interval between adjacent tones was Df (the

frequency separation between the test frequencies f1 and f2). With

intervals of Df = 44%, a series of 20 frequencies would span far

more than the audible frequency range of the rat. Therefore, we

used tones of only 12 different frequencies for this control.

Frequencies f1 and f2 were each presented with a probability of

5% to match that of a deviant in the oddball sequences, but the

other 10 frequencies were presented with a probability of 9% each.

This distribution resulted in an asymmetry between f1 and f2 on

the one hand and the other frequencies in the sequence on the

other hand, but the number of different frequencies was large

enough to mask this asymmetry [16]: in humans, it is sufficient to

‘distribute’ the probability of the standard among only 4 different

stimuli in order to remove the contribution of deviance to the

responses, and we observed similar results in rats in preliminary

experiments (data not shown). This sequence will be called the

broadband diverse sequence or ‘Diverse-broad’ in short.

In terms of tone probability and lack of regularity the Diverse-

narrow and Diverse-broad controls are comparable. However, the

responses to the oddball sequences were expected to depend on

frequency difference between the standard and the deviant. The

two controls made it possible to study the effects of frequency

separation on SSA.

The third control condition was based on the ‘Deviant-alone’

control of the MMN literature [15], and there were two such

sequences, one for each of the two main frequencies. In the

‘Deviant-alone f1’ sequence, tones of frequency f1 were randomly

Figure 1. The oddball paradigm. A. A schematic spectrographicrepresentation of the three basic sequences used in this study. In eachtrial, either f1 or f2 are presented pseudo-randomly according to theirprobability of occurrence. B. The average LFP responses in a typicalrecording site to the two frequencies of the paradigm in each of thesequences (f1 = 13.3 kHz, black, and f2 = 19.2 kHz, gray). The level was30 dB attenuation (,70dB SPL). Error bars: 6 s.e.m., shaded interval:stimulus. C. MUA Responses at the same site. The raster plots show 25presentations for each of the two frequencies, corresponding to 5% ofthe 500 tone presentations in the sequence. For the standard andequal-probability conditions, which had more than 25 presentations ina sequence, the 25 presentations were selected so that they representthe spike count distribution of all responses in the time window shown.The line graphs represent PSTHs smoothed by a 10 ms Hammingwindow.doi:10.1371/journal.pone.0023369.g001

Figure 2. SSA indices for Df = 44%. A. Scatter plots of SI2 vs. SI1 forall sets with Df = 44%. B. Histograms of CSI. The numbers near the zeroline indicate the number of CSIs smaller and greater than 0. The labels A– D mark the bins corresponding to the data in Figure 4.doi:10.1371/journal.pone.0023369.g002



presented in 5% of the presentation intervals, as in the Deviant-f1

oddball sequence, but the standard presentations were replaced by

silence. Similarly, the ‘Deviant-alone f2’ sequence presented the

deviant f2 against a silent background. These two sequences

matched the oddball sequences in terms of rarity and uniqueness

of the rare stimulus (unlike the diverse sequences), but unlike all

other sequences, in the Deviant-alone sequences there was no

interaction with any other stimulus.

Figure 3B shows the LFP responses to f1 and f2 in the three

control sequences, recorded from the same recording site as the

data presented in Figs. 1B and 1C. The responses to f1 and f2 in

each of these sequences were similar. The responses in the

Diverse-narrow sequence were smaller than those of the Diverse-

broad or the Deviant-alone sequences, demonstrating the presence

of cross-frequency adaptation. On the other hand, the responses in

the Diverse-broad and the Deviant-alone sequences were

comparable. The MUA responses shown in Fig. 3C showed

similar trends, except that cross-frequency adaptation in the

Diverse-narrow sequence was stronger for frequency f2 than for

frequency f1, possibly because f1 was closer to the best frequency

of the MUA at this site.

We therefore had seven different sequences (Deviant-f1 and -f2,

Equal probability, Diverse-narrow and -broad, and Deviant-alone

f1 and f2) that tested both f1 and f2 in six conditions. The

collection of responses to one frequency in all six conditions is

termed ‘a set’ below. Figure 4A re-plots the responses from Figs. 1

and 3, grouped by frequency rather than by presentation

sequence. It therefore illustrates the influence of context on the

responses to tones of each of the two frequencies. Figure 4B shows

responses recorded from the same recording site as in Fig. 4A, but

to another pair of frequencies. Two other typical cases from

another animal are shown in Fig. 4C and 4D, all with Df = 44%.

Note that the peak response of the MUA to f1 in the Deviant

condition in Fig. 4B occurred about 40 ms after stimulus onset,

while the peak responses to the other conditions occurred about

20 ms after stimulus onset. This was an effect of the rather long

window used here to identify the peak responses (see Methods for

justification). Because the same procedure was used in all

conditions, such long windows could increase the variability in

the data, rendering the conclusions more conservative.

The pattern of response was quite similar between sets across

animals and recording sites. Figure 5 shows a summary of the

responses to all six conditions for all the data with Df = 44%. In

order to account for general differences in response amplitude

across animals and recording sites, the responses in each recording

site and each frequency were normalized to the response to that

frequency in the deviant-alone condition of the same set, since this

is presumably the least adapted response. When a tone was rare

and well separated spectrally from other stimuli in the sequence, as

in the deviant and diverse-broad conditions, the responses it

evoked remained relatively high. On the other hand, when the

tone was presented often (as in the Standard and Equal-probability

conditions), or mixed with many tones with nearby frequencies (as

in the Diverse-narrow condition), the responses it evoked were

smaller. A 1-way ANOVA on all six conditions showed significant

effect of condition on responses. However, post-hoc comparisons

failed to show a significant difference between the three conditions

with large responses (Deviant, Diverse-broad and Deviant-alone).

The approximate equality of the responses in the Deviant and

the Diverse-broad conditions is particularly important for the

interpretation of these findings in the Discussion. Figures 5C and

5D display the LFP and MUA responses in the Diverse-broad

condition against the responses in the Deviant condition in each

recording site separately. Both responses are normalized to the

Deviant-alone condition. The responses in the Deviant condition

were correlated with the Diverse-broad in the same recording

locations for both LFP (r = 0.62, p = 7*10224) and for MUA

(r = 0.4, p = 1.5*1024). Interestingly, in some recording locations

the Deviant responses were larger than the responses in the

Deviant-alone condition (normalized Deviant responses greater

than 1, LFP: 50/214, 23%; MUA: 18/85, 21%). In many

recording locations, the Deviant responses were larger than in the

Diverse-broad condition (points below the diagonal of Figs. 5C

and 5D, LFP: 83/214, 39%; MUA: 43/85, 51%). In some

Figure 3. The control sequences. A. The three control conditions. Inthe ‘Diverse’ sequences, narrow and broad, the available frequencieswere evenly spaced on a logarithmic frequency scale. The range offrequencies was narrow (about twice Df) in the Diverse-narrowsequence, and wide (11Df) in the Diverse-broad sequence. In the‘Deviant-alone f1’ and ‘Deviant-alone f2’ sequences, the other 95% ofthe presentations were silent. B. The average LFP responses tofrequencies f1 and f2 embedded in the control sequences (samerecording site as Fig. 1). The responses in the two Deviant-alonesequences are superimposed. Error bars: 6 s.e.m., shaded interval:stimulus. C. MUA Responses at the same site.doi:10.1371/journal.pone.0023369.g003



recording locations, both conditions held (LFP: 29/214, 14%;

MUA: 17/85, 20%; see for example the MUA responses in Fig 4A,

13.3 kHz and Fig. 4D, 10.8 kHz).

Dependence of SSA on DfTo assess the bandwidth over which cross-frequency adaptation

contributes to SSA, we presented the same types of sequences with

Df’’s of 10%, 21% and 96%. Figure 6 shows typical responses to

stimulus pairs with these three values of Df, recorded from three

different animals. With Df = 10%, all conditions except Deviant-

alone showed strong adaptation, probably due to proximity

between different frequencies in the same sequence. The least

adapted responses occurred in the Diverse-broad condition that

spanned the broadest frequency band. Nevertheless, the LFP

response to each frequency when deviant was stronger than the

response to the same frequency when standard; the same occurred

for the MUA responses to f2 (11.6 kHz). With Df = 21%, the

contrast between the responses to the same frequency in different

conditions was larger and the pattern of responses became

qualitatively similar to that of Df = 44%. With Df = 96% the two

frequencies were presumably too far apart to elicit cross-frequency

adaptation, except maybe for the Diverse-narrow condition where

frequencies were still rather densely packed.

These data, for all values of Df, are summarized in Fig. 7. The

responses were normalized to the Deviant-alone responses of the

same set (as in Fig. 5). Clearly, cross-frequency adaptation strongly

affected the responses for Df = 10%, and didn’t affect much the

responses at Df = 44%. At Df = 21%, there was some decline in the

size of the responses, suggesting that this was the effective

bandwidth of cross-frequency adaptation. SSA was nevertheless

evident in both LFP and MUA at all Df, even in the strongly

reduced responses at Df = 10%.

Dependence of SSA on ISIThe dependence of SSA on ISI was studied with Df = 44%. In

two rats, responses were collected for ISI of 500 ms and 1000 ms.

In another group of four rats ISIs of 300, 700, 1200 and 1800 ms

were used. SSA was evident at all ISIs, as illustrated by the

responses in individual examples (Fig. 8A): in all of these cases, the

responses to a frequency when Deviant (red) was larger than the

responses to the same frequency when Standard (blue). The

population average for each group (Fig. 8B) showed a decrease in

SSA as ISI increased (Fig. 8C), but SSA was significant even with

ISIs of 1800 ms both in individual recording sites and in the

population average. To check the significance of the different

apparent trends of response magnitudes in the two groups of rats, a

3-way ANOVA was performed, the factors being the groups of

rats (two levels), conditions (six levels), and short vs. long ISIs (two

levels: in the first group 500 ms was considered as short ISI,

1000 ms as long ISI, while in the other group we coded 300 ms

and 700 ms as short ISIs, 1200 and 1800 ms as long ISIs). The

main effect of ISI on the size of LFP responses was not significant

(F(1,868) = 3.31, p = 0.07), and even the interaction between ISI

and the group of rats was only borderline significant

(F(1,866) = 4.35, p = 0.04). We conclude that the difference in

trends in the two groups, even if present, is small.

Modeling SSAThe data suggest that the responses to tone sequences are

shaped by ‘adaptation channels’ whose width is about 20% of their

center frequency. To check whether this mechanism is sufficient to

account for the data, we turned to modeling. The model describes

the effects of tone presentations at all frequencies on the responses

to tones at the center frequency, fo, of the adaptation channel.

Figure 4. Typical responses to all conditions (Df = 44%). A. Responses of LFP and MUA recorded at the same site as Figs. 1 and 3 to f1 and f2 inall stimulus conditions, sorted by frequency. Insets: the amplitudes of the responses, normalized by the responses at the corresponding deviant-alonecondition. The raster plots display only 25 representative responses in the standard and equal-probability conditions, as in Fig. 1. B. Responsesrecorded in the same recording site as A to sequences with different f1 and f2. C,D. Additional examples of the responses to all sequences, recordedfrom another rat.doi:10.1371/journal.pone.0023369.g004

Figure 5. Summary of the responses to all conditions(Df = 44%). A and B. Average (bar plots) and distributions (summarizedas box plots) of the responses (normalized with respect to thecorresponding deviant-alone response) to both frequencies in each ofthe six conditions, in all sets recorded with Df = 44%. The number ofcases included in each column is displayed underneath it (not all setsincluded all the six conditions). A. LFP, including 114 sets recorded from60 individual sites in 22 rats. B. MUA, including 49 sets recorded from 35sites in 18 rats. C and D. The normalized response to in the Deviantcondition (abscissa) versus the normalized response to the same tone inthe Diverse-broad condition (ordinate). Each point represents one ofthe main frequencies (either f1 or f2) of a set in a specific recording site.C. LFP, (r = 0.62, df = 212, p,,0.001). D. MUA (r = 0.40, df = 83,p,0.001).doi:10.1371/journal.pone.0023369.g005



The model is inspired by models of adaptation by depletion of

synaptic resources [18]. The relationship between the model

which is heuristically developed below and a formal model of

resource depletion is clarified in Text S1. The model assumes that

each tone presentation depletes synaptic resources available to

produce responses. The interplay between synaptic depletion and

recovery during the silence between two stimuli (considered as

fixed here, since only the data with ISI = 300 ms was modeled)

Figure 6. Responses as a function of Df. A. LFP and the corresponding MUA responses to the two frequencies in a paradigm with Df = 10%.B. Df = 21% (from another animal). C. Df = 96% (another animal). Same conventions as in Fig. 4.doi:10.1371/journal.pone.0023369.g006

Figure 7. Summary of the responses at all Df. A. The population average of normalized LFP responses to the six conditions for four differentvalues of Df. B. Scatter plots of SI2 vs. SI1 for LFP data from all recording sites as a function of Df. C,D. Same as A,B, but for MUA data. The scatter plotsof SI2 vs. SI1 at Df = 44% are those appearing in Fig. 2A, and have been replotted here as well for completeness.doi:10.1371/journal.pone.0023369.g007



results in a steady state response after a few stimulus presentations

[19]. The steady state response to tones of frequency f0 within a

sequence composed only of tones of frequency f0, normalized by

the unadapted response, will be denoted by B,1. The responses to

very rare tones at frequency f0 embedded in a sequence of tones of

a frequency f that is different from f0 should still be adapted, but

only partially. To capture this frequency dependence, the model

posits that the normalized response to a tone at frequencyfo after a

long sequence of tones of frequency f is BK fo,fð Þ, where the kernel K

is a (non-normalized) Gaussian,

K fo,fð Þ~ exp {log2f {log2foð Þ2

2s2

!:

The half-width of the cross-frequency adaptation band, s, is the

main parameter of interest of the model. For f = f0, the K(f0,f0) is 1

and we recover the steady state response level B. As f gets farther

away from f0, K(f0,f) decreases to 0 so that the normalized response

level BK fo ,fð Þ approaches 1 (the unadapted response).

The sequences we used consisted of tones of multiple

frequencies. To combine the effects of all of these frequencies

multiplicatively, the average contribution of all the presentations of

tones at frequency f that appeared with probability pf in the

sequence was modeled as Bpf K fo ,fð Þ. The overall adaptation depth

is a product of the ideal unadapted response, A, and the

adaptation contributed by each of the frequencies that appeared

in the sequence:

R foð Þ~A:B

Pf

pf K fo ,fð Þ

,

where the sum is taken over the frequencies of all tones in the

sequence. The last formula should be considered as an estimate of

the steady-state adapted response to frequency fo during a ‘typical’

stationary sequence of stimuli, in which the different frequencies

were randomly intermixed with the overall proportions given by

pf . The model has three parameters: B, s and A.

To motivate the comparison that will be made between the

model and the data, we start by giving a semi-quantitative

description of the behavior of the model applied to the type of

sequences used here. Obviously, the responses that are predicted

by the model are inversely related to the sum in the exponent,

which will be called ‘the adaptation load’. In the Standard, Equal-

probability and Diverse-narrow conditions, it is clear why the

adaptation load is relatively large: f0 and/or nearby frequencies,

for which the kernel is large, are highly probable. The interesting

cases consist of the three conditions that give rise to large

responses: Deviant-alone, Diverse-broad, and Deviant (in a

decreasing order of the average responses).

The adaptation load is least (and therefore the predicted

responses largest) in the Deviant-alone condition, where it is equal

to 0:05K(f0,f0)~0:05, 0.05 being the probability of the deviant,

and K(f0,f0)~1 (see Figs. 9A, green bar and 9B; in Fig. 9A the

adaptation channel is centered on frequency f1). In the Diverse-

broad and Deviant conditions the adaptation load includes this

term as well, but has additional contributions.

Figure 8. Respones as a function of ISI. A. Average LFP responses at five different ISIs. The examples, from different animals, show the responsesto the low frequency (f1) in each pair. B. The population average of the responses to different ISIs for the two groups of rats (continuous and dashedlines) used in testing the effects of ISI. The error bars are s.e.m. For more details, see text. C. The distributions of the individual CSIs at each ISI. Circlesare ‘common’ averages, computed as the sum of all numerators of contrasts in each ISI value, divided by sum of all denominators.doi:10.1371/journal.pone.0023369.g008



In the Diverse-broad condition, the additional terms in the

adaptation load span a relatively large frequency band. If the

width of the kernel K(f0,f) is on the order of the spacing between

the frequencies used in the sequence, only a few terms will

contribute significantly to the adaptation load, because of the

shape of the kernel. For example, if the width of the kernel is

s = 0.34 (which is a typical value for the data, see below) and the

spacing between frequencies is Df = 44%, only two additional

terms will contribute significantly to the adaptation load, one

frequency on each side of the center frequency (Fig. 9A, yellow

bars: the two contributing frequencies are f2, with probability

0.05, and fE, with probability 0.09). The adaptation load in the

Diverse-broad condition is therefore approximately 0:05K(f0,f0)z0:14K(f0,f0 � 1:44). Thus, the normalized response in the

Diverse-broad condition is expected to be about B0:14K(f0,f0�1:44).

Since in the majority of cases the normalized response in the

Diverse-broad condition is smaller than 1 (Figs. 5C, 5D), it follows

that

K(f0,f0 � 1:44)w0

must hold – in other words, the width of the adaptation channel is

non-zero.

In the Deviant condition, the additional term in the adaptation

load is solely due to the standard, and the adaptation load is

0:05K(f0,f0)z0:95K(f0,f0 � 1:44) (Fig. 9A, red and blue bars;

Fig. 9B). The normalized response to the Deviant is therefore

B0:95K(f0,f0�1:44). Intuitively, the adaptation load in the Deviant case

will be larger than that in the Diverse-broad condition, because in

the Deviant condition, all sound presentations that contribute to

the adaptation have been brought closer to the deviant frequency

(the blue bar is as high as the sum of probabilities of all the yellow

bars except for the one at f1). In fact, under the assumptions we

made about the rate of decrease of K, the model makes the

prediction that the response in the Deviant condition should be

smaller than in the Diverse-broad condition, and by appreciable

amounts: with the approximations made above, if the normalized

response in the Diverse broad condition is 0.9, the normalized

response in the Deviant condition is expected to be less than 0.6

(Fig. 9B).

The normalized responses in the Deviant and in the Diverse-

broad conditions can be used to derive estimates for the width of

the adaptation channel. For that purpose, it is necessary to

estimate B, which we do by using the normalized response to the

Standard condition. In the Standard condition, f0 is presented in a

probability of 0.95, complemented by the other frequency, Df

apart. The adaptation load is therefore 0:95z0:05K(f0,f0 � 1:44),and the normalized response is therefore B0:9z0:05K(f0,f0�1:44). The

kernel is smaller than 1, so the normalized response to the

Standard lies between B0.95 and B0.90. The calculations below set

the Standard responses to B0.90; setting the Standard responses to

B0.95 resulted in practically the same values. We estimated the

width of the adaptation channel using the responses to the

Diverse-broad condition and separately using the responses to the

Deviant condition. In both conditions, we calculated the predicted

normalized response as a function of the width of the adaptation

channel, and selected the width that corresponded exactly to the

measured response. For this analysis, we used only the data for

which the responses in the Deviant condition were smaller than

the responses in the Diverse-broad condition, and both were

smaller than in the Deviant-alone condition (these conditions are

necessary for a solution to exist with B,1 and K(f0,f )w0). It

should be remembered, however, that these cases comprise only

about half of the total data (see Fig. 5).

Figure 9C shows the two estimates of the width of the

adaptation channel plotted against each other for the LFP data

collected with Df = 44%. Clearly, the Diverse-broad responses

required a consistently wider adaptation channel than the Deviant

responses. To recapitulate, this discrepancy is due to the

experimental findings that the responses in the Diverse-broad

condition are smaller than in the Deviant-alone condition

(requiring a large enough adaptation bandwidth), but are about

the same size as in the Deviant condition (an equality that can hold

only for small adaptation bandwidth). Thus, these two findings are

Figure 9. A simple adaptation model. A. A schematic plot of theadaptation channel centered on fo = f1 with s= 0.34 (left ordinate) andthree of the sequences that included this frequency with probability of5% (right ordinate), all three with Df = 44%. The Deviant f1 sequence(probability of f1 marked in red) included also tones at frequency f2with a probability of 95% (blue). Frequency f2 lies within the effectiverange of the kernel centered on f1, thus causing a significant amount ofadaptation. The Diverse-broad sequence includes f2 with probability of5% and 10 other frequencies with a probability of 9% each (yellow).These additional frequencies are marked here fA to fJ (some of them areomitted from the figure due to space limitations). The Deviant-alone f1sequence (green) does not contain any other frequency. The barsrepresenting tone probabilities at f1 and f2 for the different sequencesare shown side by side. B. Upper bars - the adaptation load for the threeconditions,

Pf

pf K fo,fð Þ. Lower bars – the predicted normalized steady-

state response. Note that the Deviant response is appreciably smallerthan the Diverse-broad response, which is about the same as theDeviant-alone response. In the measured data, the Deviant and Diverse-broad responses had about the same size. C. The half-width of thekernel computed for each of the LFP recording sites with Df = 44%when derived from Deviant responses (abscissa) plotted against thehalf-width of the kernel derived from Diverse-Broad responses(ordinate). The normalized adaptation load when the center frequencywas the Standard was assumed to be 0.90, the minimum possible. Theresults were virtually the same when the normalized adaptation load forthe standard was assumed to be maximal, 0.95.doi:10.1371/journal.pone.0023369.g009



incompatible with a model of adaptation in narrow frequency

channels.

The analysis shown in Fig. 9 used only a small part of the data –

only data with Df = 44%, using the responses to only four out of

the six conditions in which the tones were tested, and only half of

the points. Furthermore, the parameters of the model were

estimated suboptimally. In the rest of this section, we overcome

these limitations. We first fitted a model to all the data of each

recording site. As will be seen below, these models gave rather

good fits to the data, but their error, although close to, was often

larger than the best possible error. We reasoned that this bias had

to do with the two conflicting requirements discussed above: the

reduction in the responses to the Diverse-broad condition relative

to the responses in the Deviant-alone condition, but the almost

equality of the Diverse-broad and Deviant responses. We therefore

removed each of the test conditions in its turn from the training set

and refitted the models, using them to predict the responses in the

left-out condition. As expected, removing either the Diverse-broad

or the Deviant conditions resulted in the greatest changes in both

the quality of the fit and in the estimated width of the adaptation

channel, and also with the largest deviations between predictions

and measured responses to the left-out conditions.

We fitted the model separately in each individual recording site,

using all the data recorded in that site, including all frequency

pairs at all Dfs. The discrepancy between the model and the actual

responses was measured by the squared differences between each

of the measured responses and the model prediction of that

response. These discrepancies were added over all the responses to

each of the two frequencies in each of the six conditions (which

differed in the probabilities of the tone, its accompanying tones in

the sequence, and the frequency separation between these tones) in

all the sets that were presented at that recording site (up to 48

different responses, since not all recording locations were tested

with all conditions and Dfs). To account for the different number

of stimulus presentations that were used to determine each

response, each term was weighted:

e2~X

c

wc(rc{mc)2,

where the sum is over up to 48 terms, rc is the measured response

in each condition, mc is the model prediction for that condition,

and wc is the weight (square root of number of stimulus

presentations at that condition).

The resulting total weighted squared error e2 between the

model response and actual response was then minimized in the

following way. For each value of s, the half width of the

adaptation channel, e2 was minimized to produce the optimal Aand B (using the fit function from the Matlab curve fitting

toolbox). This made e2 into a function of s only. A one-

dimensional search was used to find the value of s that minimized

e2. We didn’t allow sto decrease below 0.05 or increase above 4.0.

Cases in which sreached one of these boundaries were omitted

from further analysis (7/91 LFP sites, 8%; and 6/43 MUA sites,

16%). All further analysis considers only recording sites in which

the parameters were successfully estimated.

The parameters A and B were also determined as part of the

fitting process. The parameter A is a scale parameter which

depends on the overall size of the responses. Since we applied the

model to responses that were normalized, each to its correspond-

ing deviant-alone responses in each set and frequency, A was

expected to be, and indeed was, around 1, independently of

recording site and frequency. B was essentially equal to the

normalized response to the standard, since the exponent in the

defining formula of the model in that case is very close to 1.

Neither parameter is further analyzed here.

The model accounted reasonably well for the data, given its

simplicity. Figure 10 plots the fitted responses against the

measured responses for all the recording sites and all types of

sequences. Clearly, the model qualitatively captured the main

aspects of the data: the fitted values to the Deviant-alone, Diverse-

broad and Deviant (in green, yellow and red, respectively) were

larger than to the other conditions. Also, overall, the individual

fitted values scattered around the diagonal, as they should.

In order to judge the goodness of fit of the model more

quantitatively, we compared the mean error of the model, e2, with

the weighted sum of squared standard errors of the mean

responses

se2~X

c

wcsec2,

where the sum is over the same conditions as e2, wc denotes the

same weights used in calculating e2, and sec is the standard error of

the measured response, rc. This expression is an estimate of the

error of the best possible model for the responses, in which the

response to each condition is fitted by its observed mean value.

Figure 11A shows the histograms of the ratios between the fit

error,e2, and the sum of squared standard errors of the responses,

for all the relevant recording sites. The curves plotted on top of the

histograms show the average expected distribution of the same

ratios under the assumption that the model error is the minimal

possible: since these are ratios of variances, assuming approximate

Gaussian distribution for the errors, they should roughly follow an

F(n,n) distribution with n, the number of degrees of freedom being

the number of responses that were fitted by the model in each site.

The average of all the F distributions for the modeled sites, and the

corresponding 95% critical value, should give a rule of thumb for

the goodness of fit. For the LFP data, 61% of the cases had error

ratio below the critical value of the average F distribution, and for

the MUA data, 81% of the cases had error ratio below the critical

value. Thus, this 3-parameter model fitted much of the data quite

well, although it also showed a consistent bias.

We were mostly interested in the estimated half-widths of the

adaptation band, s. The distribution of s for the different sites is

shown in Fig. 11B. For the LFP responses, the median of s was

0.27 octave, with interquartile range of 0.19 – 0.40 octave. For the

MUA responses, the median was 0.29 octave, with an interquartile

range of 0.21 – 0.43 octave. Thus, the model suggests that in as

much as SSA is due to adaptation in narrow frequency channels,

significant across-frequency adaptation should occur once the

tones are within 1/3 octave of each other.

Having established that the model produced a reasonable

approximation to the data, we analyzed the fit in more details

using the leave-one-out procedure described above. The general

trends were similar for LFP and MUA data; the description below

summarizes the main findings of the leave-one-out procedure,

while leaving the interpretation to the Discussion.

The fits in the leave-one-out conditions had bandwidths and

errors that differed from those of the fit to the whole data.

Figure 12A shows the distribution of ratios of the adaptation

bandwidths in the leave-one-out models and in the models fitted to

all the data. Removing the Deviant responses from the training

data resulted in a consistent increase in the fitted bandwidth. An



Figure 10. A comparison between the model predictions and the experimental observations. Scatter plots of all the measured responses(abscissa) versus the corresponding fits from the model based on all the data (ordinate) for each value of Df. A. LFP data. B. MUA data.doi:10.1371/journal.pone.0023369.g010

Figure 11. Model error and half-width of the adaptation channel. A. Distribution of the sum of squared errors between responses and fittedvalues, divided by the sum of squared s.e.m of the responses. The terms of both sums were weighted by the square root of the number ofpresentations of the corresponding stimulus. For comparison, the thin line indicates the average of the F(n,n) distributions associated with each ofthe sites, where n, the number of degrees of freedom, is the number of responses that were fitted by the model at that site. Vertical dotted line - the0.05 critical value of the average F distribution. The majority of the models (51/84 of the models fitted to LFP responses, 61%, and 28/69 of thosefitted to MUA responses, 81%) had an error ratio below this limit. B. Distribution of the half width of the adaptation channel, s, in all recording sites atwhich the fitting procedure converged successfully.doi:10.1371/journal.pone.0023369.g011



opposite effect was evident when removing the Deviant-alone or

the Diverse-broad responses from the training data, resulting in an

overall decrease of the bandwidth of the adaptation channel. Such

consistent effects were not seen for any of the other conditions.

The mean error over the training data was lower, as expected,

when part of the data was left out (Fig. 12B). The two conditions

that reduced the error most when left out, relative to the fit using

the full data, were the Deviant and Diverse-broad conditions. In

fact, the reduction in error when removing the Deviant responses

from the fit was if anything larger than when removing the

Diverse-broad responses (one-tailed t-test, p,0.05 for LFP, ns for

MUA).

The predictions of the leave-one-out procedures showed some

consistent bias relative to the actual measurements. A 2-way

ANOVA on condition and Df confirmed that the bias showed a

significant effect of condition (LFP: F(5,1735) = 31, p,0; MUA:

F(5,615) = 12,p,0), a non-significant main effect of Df (LFP:

F(3,1735) = 0.99, n.s.; MUA: F(3,615) = 0.95, n.s.), but a highly

significant interaction between condition and Df (LFP:

F(15,1735) = 4.9, p,0; MUA: F(15,615) = 2.38, p = 0.0024).

Figure 13 compares the average measured responses with the

predicted responses based on each of the leave-one-out models for

the LFP responses. The general pattern of results is very similar for

the MUA models. In each panel, the full-colored lines compare the

measured (thin line) and predicted (thick line) responses to one left-

out condition (indicated both in the title of the panel and through

the color code). The pastel-colored lines show the same

information for the five conditions that have been used to estimate

the parameter of the model. As a rule, these responses are well-

estimated, as expected from the ability of the model to account

well for the data.

Taking out the responses in the Equal condition (Fig. 13B) and

the diverse narrow condition (Fig. 13C) did not affect much the

predictions, which fitted the measured responses very well on

average. Taking out the Standard responses from the training set

(Fig. 13A) resulted in over-prediction of these responses (thick blue

line, representing the predictions, is overall above the thin blue

line, representing the average responses). Conversely, taking out

the Deviant alone responses from the training set resulted in

predictions that were on average too small (Fig. 13F).

Most importantly, there were consistent consequences to

removing the Deviant or the Diverse-broad responses from the

training data. When the Deviant condition was left out, the

resulting predictions of the responses in the left-out Deviant

condition were consistently smaller than the actual responses

(Fig. 13D, compare thin and thick red lines). On the other hand,

when the Diverse-broad responses were left out, the predictions of

the responses in the left-out Diverse-broad condition were

consistently too high (Fig. 13E, compare thin and thick yellow

lines).

Discussion

Summary of the results and relationships with previousstudies

In this paper we studied the responses to low probability sounds

in the auditory cortex of halothane-anaesthetized rats and the

influence of the auditory context on these responses. The strength

of the response to a given frequency depended on its probability of

appearance in the sequence, as well as on the frequency

composition of the rest of the sequence and on the ISI. Thus,

when a frequency appeared in 5% of the tone presentations the

responses were stronger than when it appeared in 95% of the tone

presentations in 2-tone sequences (Figs. 1–7). This effect was large

and robust for frequency differences of 21% and more, and

significant at 10% (Figs. 6,7). Furthermore, responses depended on

tone probability even with ISIs as long as 1800 ms, although this

effect decreased somewhat at the longest ISIs (Fig. 8).

As a result, as long as the frequency separation between nearby

tones was larger than about 20%, tones in low probability

conditions elicited large responses (Deviant-alone, Deviant and

Diverse-broad conditions). In contrast, high probability conditions

(Standard and Equal-probability) gave rise to smaller responses.

Even in a low probability condition, significant reduction in the

responses could occur when the frequencies in the sequence were

densely spaced (Diverse-narrow condition). This pattern of results

was apparent in both LFP and MUA.

These results are consistent with previous studies of oddball

responses. Ulanovsky et al. [5,6] studied the responses of single

neurons in cat auditory cortex to oddball sequences. The results of

these papers are similar to those presented here, although the tone

duration was 230 ms, substantially longer than the 30 ms used

here. The ISI in the main data of the papers of Ulanovsky and

colleagues, 730 ms, lies within the range of ISIs in which we

showed significant effects, and the longest ISI at which they found

significant effects was 2 s (while here we show significant responses

at 1.8 s, but didn’t test longer ISIs).

Lazar and Metherate [20] demonstrated SSA in epidural

potentials recorded from rat auditory cortex with Df of 100%

and higher, but had full cross-frequency adaptation (‘spectral

Figure 12. Comparing model error and half-bandwidth be-tween all-data and leave-one-out models. A. Distribution of theratios of the widths of the adaptation channel derived from a model forwhich responses to one condition were left out of the training data, andthe width derived from a model that was fitted to all the data. Thecolors indicate the left-out condition. B. Distribution of ratios betweenthe sum of squared errors of the model with the correspondingcondition left out and the sum of squared errors of the model that wasfitted to all the data (sums of squared error computed for the trainingdata only).doi:10.1371/journal.pone.0023369.g012



interactions’) with Df of 11% or smaller. They used a slower rate of

presentation, longer stimuli and higher probability of the deviant

stimulus relative to those used in the current study. The pattern of

their results is roughly consistent with ours, and the lower

sensitivity in their study may be due to the large-scale averaging

inherent in epidural recordings.

Malmierca and co-workers demonstrated significant SSA in rat

inferior colliculus (IC) [7,21] and in rat medial geniculate body

(MGB) [10]. SSA with similar characteristics was also found in the

MGB of mice [9]. Although adapting neurons were found in all IC

and MGB subdivisions, SSA was much weaker and essentially

non-existent at the rates used here in the ventral division of the

MGB in rats [10]. It was mostly found outside the ventral division

of the MGB in mice as well [9]. Given that the ventral division of

the MGB is the major ascending input to the auditory cortex, these

results suggest that much of the SSA measured in auditory cortex

is actually generated in auditory cortex, with a possible small

contribution from the MGB. Indeed, Szymanski et al. [22] showed

that SSA in rat auditory cortex increased away from layer IV,

suggesting an important contribution of cortical processing to

cortical SSA. All of these studies used only the Standard, Deviant

and Equal conditions, which makes it impossible to dissect out the

contribution of pure adaptation to the Deviant responses, as we

could do using the additional control conditions in the current

paper.

Two recent reports demonstrated SSA in auditory cortex of

awake rats as well. Von Behrens et al. [11] found SSA that was

somewhat weaker than that reported here, but they used longer

tones and longer ISIs than those used for most of the data reported

here. Farley et al. [12] used, in addition to the Deviant condition,

the so called ‘control’ condition of the MMN literature, which is

equivalent to the ‘Diverse-broad’ condition used in this paper [17].

They found significant SSA to frequency shifts, and found the

responses to tones in the Deviant and in the Diverse-broad

conditions to be essentially equivalent on average. These findings

are rather similar to those we report here.

Farley et al. [12] concluded that there is no deviance sensitivity

in A1 of rats. However, the additional conditions we used in this

paper allowed us to show that under a model of pure adaptation

the responses in the Deviant condition should be smaller than in

the Diverse-broad conditions (Fig. 9). Thus, the observed near

equality of the responses in these two conditions (Figs. 5C and 5D)

is actually surprising, and indicates the presence of true deviance

sensitivity. We turn now to justify these statements.

Mechanisms of SSAAdaptation of the responses to tones of a given frequency is

obviously induced by the presentation of the same tone frequency,

but also by the presentation of tones of adjacent frequencies.

Cross-frequency adaptation became significant at frequency

separations between 21% and 44% (Fig. 7). This range is

substantially narrower than the tuning width of either LFP or

multiunit clusters (e.g. [23]), but is consistent with the width of the

adaptation channels estimated by the models (Fig. 11B). Thus,

Figure 13. Comparing the leave-one-out model predictions with experimental observations. A-F. Comparison of the average LFPresponses at the six stimulation conditions and four values of Df (thin lines, the same in all panels) with the predicted responses from models in whichone condition (denoted at the title of each panel) was left out of the training data (thick lines). Error bars: s.e.m. The data of the left-out condition isdisplayed in saturated color, while the data for the conditions that were part of the training set are displayed in faded colors.doi:10.1371/journal.pone.0023369.g013



adaptation occurs within channels extending about 1/3 octaves on

either side of the center frequency.

Adaptation in narrow frequency channels, however, makes two

predictions that are not fully born out even in with a cursory look

through the data. First, the responses in all conditions should be

smaller than the responses in the Deviant-alone condition; and

second, that the responses in the Deviant condition should be

smaller than in the Diverse-broad condition (Figs. 9A and 9B).

The failure of both predictions is illustrated in Figs. 5C and 5D.

First, a substantial number of recording sites had responses in the

Deviant and Diverse-broad conditions that were larger than in the

Deviant-alone condition. While these deviations could be argued

away as noise in the measurement of the responses, the same

figures also illustrate the near-equality of the responses in the

Deviant and in the Diverse-broad condition. This near-equality

leads to divergent estimates of the adaptation channel when using

each of the two types of responses separately (Fig. 9C).

The leave-one-out procedure highlighted some additional

features of the data. Four conditions resulted in consistent

differences between the predictions to the left-out conditions and

the actual measured responses in the same conditions. Two of

these are easy to explain: leaving out the standard responses led to

over-prediction of these responses, while leaving out the Deviant-

alone conditions led to under-prediction of these responses. These

effects presumably occurred due to the need to extrapolate the

extreme responses (smallest response in the case of the Standard,

largest in the case of the Deviant alone) based on the other

responses, leading to ‘regression to the mean’ of the predicted

responses. The reduction in the predicted responses to the

Deviant-alone condition accounts for the effects of removing the

Deviant-alone conditions from the training data on the estimated

bandwidth (Fig. 12A). It is easy to see that these responses are an

increasing function of the scale A and a decreasing function of the

bandwidth s. Thus, under-prediction of A (which is roughly the

response in the Deviant-alone condition) requires a decrease in the

bandwidth s in order to account for the responses that remained

in the training set.

More interestingly, leaving out the Deviant responses resulted in

their under-estimation (Fig. 13D), but also to a significant increase

in the estimated bandwidth of the models (Fig. 12A) and the

largest reduction in the error of the fit among all left-out conditions

for the LFP data (Fig. 12B). For the MUA data, the median

reduction in the error was larger in the Diverse-broad condition,

although non-significantly so; on the other hand, the 75%

percentile of the error reductions of the Diverse-broad left-out

models was much smaller than that of the Deviant left-out models.

Removing the Diverse-broad responses resulted in the opposite

trend: responses were over-predicted and the estimated bandwidth

of the models became narrower.

Thus, it seems that the Deviant responses are somehow

inconsistent with the rest of the data – removing them modified

significantly the main parameter of the model, the bandwidth of

cross-frequency adaptation, and reduced substantially the error of

the fits. At the root of this inconsistency lies the fact that the

responses to the deviant and diverse-broad conditions had about

the same strength, but that both were typically somewhat smaller

than the responses in the Deviant-alone condition. These findings

are hard to accommodate in a model based on adaptation in

narrow frequency channels (Fig. 9).

The contradiction between these two requirements is reflected

in the predictions of the left-out conditions. In a model that was

fitted without the Diverse-broad responses, there was a tendency

of the estimated width of cross-frequency adaptation to be

narrower, pulled in this direction by the inclusion of the Deviant

responses in the training data. The reduction in the width of the

adaptation channel resulted in predictions of the Diverse-broad

responses that were too large, since most stimulus presentations in

this condition fell outside the effective width of cross-frequency

adaptation. The concomitant decrease in adaptation bandwidth

and over-prediction of the Diverse-broad responses can be

observed in Fig. 12A and Fig. 13E, respectively.

The opposite tendencies can be observed in the models in which

the Deviant responses were excluded from the training data. The

adaptation bandwidth was pulled to larger values by the reduction

in the Diverse-broad responses relative to the Deviant-alone

responses. As explained above, a wider adaptation bandwidth

results in under-predictions of the deviant responses. The two

effects can be observed in Figs. 12A and 13D.

SSA, deviance detection and Mismatch NegativityThe inconsistency between the responses in the Deviant and in

the Diverse-broad conditions suggests that somewhat different

mechanisms operate in these two conditions. The Diverse-broad

condition is (almost) symmetric with respect to all tone frequencies

that appear in it; this is true also for the Diverse-narrow and Equal

conditions. Thus, it makes sense to hypothesize that it is the

composition of the oddball sequence, with its large asymmetry

between the probabilities of the two tones, which engages special

mechanisms. This assumption is further supported by Fig. 12B: the

error of the fit was reduced more when removing the Deviant

responses from the training data than when removing the

responses to other conditions (at least for the LFP data, and for

the more extreme cases of the MUA data), hinting at the fact that

the Deviant responses are special. The contribution of the

hypothetical mechanisms that are engaged in the Deviant

condition to the response is large: adaptation in narrow frequency

channels, fitted to all other conditions, accounted for only half of

the average increase in Deviant relative to the Standard responses

(Fig. 12D, compare thin and thick red lines).

Although we do not know what are the additional mechanisms

that are engaged in the Deviant condition, the model presented

here suggests one candidate: a dynamic, rather than fixed,

adaptation bandwidth. The hypothesis is that the repetition of

the standard stimulus causes a reduction not only in the responses

to the standard, but also in its effects on the responses to other

stimuli, causing the adaptation channel centered on the deviant

frequency to become narrower. As a result, the deviant stimulus

elicits similar responses to those of a stimulus with the same

probability in the Diverse-broad condition, where such narrowing

of the adaptation channel does not occur, but where the

adaptation channel is stimulated much less. The enhanced

responses in the Deviant condition may be interpreted as signaling

the detection of the change, or deviance, that a deviant tone

represents relative to the regularity set by the standard.

An important component of the auditory ERPs, the MMN, is

believed to signal deviance detection in humans. There are many

similarities between SSA in auditory cortex and MMN [13],

suggesting that SSA lies upstream of MMN generation, although

the early timing of SSA in auditory cortex indicates that the

responses studied here are not those that directly cause the

currents which are measured as MMN on the scalp. Furthermore,

Farley et al. [12] failed to produce SSA in response to level

deviants (but see [6]) and duration deviants, and showed that SSA

in auditory cortex does not depend on NMDA receptors, while

MMN does. These results emphasize the distance that separates

SSA in auditory cortex and MMN.

In contrast with the negative results of Farley and co-workers,

we demonstrate here that SSA in auditory cortex has one of the



truly distinguishing characteristics of MMN [24,25] – true

deviance detection. Adaptation in narrow frequency channels

should not be considered as true deviance detection, since it is

sensitive to the rarity of the deviant but not to the regularity of the

standard. The current results are the first demonstration that

oddball sequences might engage true deviance-detection mecha-

nisms, rather than only adaptation in narrow frequency channels,

already at the level of auditory cortex. Therefore, these findings

strengthen the case for SSA in auditory cortex as an important

contributor to the generation of MMN.

Materials and Methods

PreparationWe used 26 adult female Sabra rats weighing 140–250 gm and

2 male Sabra rat juveniles, p27 and p34, for this study (Harlan

Laboratories Ltd., Jerusalem, Israel). The joint ethics committee

(IACUC) of the Hebrew University and Hadassah Medical Center

approved the study protocol for animal welfare (protocols NS-06-

10041-3, NS-08-11349-3). The Hebrew University is an AAALAC

International accredited institute.

Animals were initially anesthetized with an intramuscular

injection of ketamine (20–70 mg/kg, Ketaset, Fort Dodge Animal

Health, Fort Dodge, IA) and medetomidine (0.05–0.5 mg/kg,

Domitor, Orion Pharma, Espoo, Finland). Additional smaller

doses of ketamine were administered as needed to maintain

anesthesia during surgery. Surgical level of anesthesia was verified

by pedal-withdrawal reflex.

The trachea was cannulated and the animal was fixed to a

custom-made head holder [26], that left the scalp and ears free. The

animal was ventilated through the tracheal cannula (10-15 mmH2O

peak inlet pressure, 47/min, 15–30cc per stroke, 0.7–1.4 L/min) by

a mixture of O2 and halothane (Rhodia Organique Fine Ltd.,

Bristol, UK) using a small-animal ventilator (model AWS, Hallowell

EMC, MA), and a halothane vaporizer (VIP 3000, Matrx, NY).

Once the animal was ventilated, ketamine anesthesia was

discontinued, and halothane volume concentration was regulated

around 0.5% to have a sufficient anesthesia depth. Throughout the

experiment, respiration quality was monitored by continuously

measuring the CO2 concentration in the tracheal cannula (Micro-

cap, Oridion Medical Ltd., Jerusalem, Israel). The depth of

anesthesia was judged by the lack of motion and resistance to the

respirator, and levels of anesthetics and ventilation pressure were

adjusted accordingly. Body temperature was monitored and

maintained at 36–38uC using a rectal thermistor probe and a

feedback-controlled heating pad (FHC Inc., ME).

The left temporal portion of the skull was cleaned from skin,

muscles, and connective tissue. A craniotomy was performed over

the estimated location of left auditory cortex – 2.5mm–6.5mm

posterior to and 2mm–6mm ventral to bregma [27]. A copper

wire hook implanted in the neck muscles was used as the electrical

reference.

Electrophysiological recordingsWe recorded extracellularly from the auditory cortex using 1–4

glass-coated tungsten electrodes (Alpha-Omega Ltd., Nazareth-

Illit, Israel), or a single micropipette. Metal electrodes were

assembled together with separations of ,600 microns. The

electrodes were lowered into the cortex using a microdrive (MP-

225, Sutter Instrument Company, Novato, CA).

The electrical signals were pre-amplified (610), filtered between

3 Hz and 8 kHz to obtain both LFP and action potentials, and

then amplified again, for a total gain of 65000 (MCP, Alpha-

Omega, Nazareth Illit, Israel), to yield the raw signals. The raw

signals were sampled at 25 kHz and stored for off-line analysis.

The analog signals were also sampled at 977 Hz after anti-aliasing

filtering (RP2.1, TDT, Tucker-Davis Technologies, Alachua, FL),

stored for LFP analysis, and used for online display.

Sound stimulation systemAll experiments were conducted in a sound-proof chamber

(IAC, Winchester, UK). Sounds were synthesized online using

Matlab (The Mathworks, Inc., Natick, MA), transduced to voltage

signals by a sound card (HDSP9632, RME, Germany), attenuated

(PA5, TDT), and played through a sealed speaker (EC1, TDT)

into the right ear canal of the rat. In several animals, an acoustic

calibration was performed as follows. A post-auricular incision was

made and the meatus was cut as close to the skull as possible. A

custom-made brass cone with speaker and microphone inlets was

fastened so as to cover and seal the meatus in front of the tympanic

membrane. The microphone (model EK-3133-000, Knowles,

England) was previously calibrated against a calibrated condenser

microphone (Type 2633, Bruel & Kjær, Denmark). Frequencies in

the range of 1–64 kHz, with 20 frequencies per octave, were

presented through the sealed speaker. When harmonic distortion

in the microphone signal was larger than 0.5%, presentation level

was reduced; the largest attenuation level at which harmonic

distortion was lower than 0.5% was measured as well, and was

found to be higher than 80 dB SPL for all tested frequencies. The

intensity of stimuli during the experimental paradigm did not

exceed this value. The calibration of the system was found to be

stable across animals. The intensity deviations between the pairs of

frequencies that are reported here did not exceed 610dB near the

tympanic membrane. In those experiments where calibration was

performed, the speaker, the microphone and the brass cone were

left in their position throughout the experiment.

For pure tones, attenuation level of 0 dB corresponded to about

100 dB SPL. Noise stimuli were synthesized at a spectrum level of

250 dB/sqrt(Hz) relative to pure tones at the same attenuation

level.

Experimental procedureRecording sites were selected by their response to a broad-band

noise (BBN). We searched for sites while continuously presenting

200 ms BBN bursts (0–50 kHz) with inter-stimulus time interval

(ISI, onset to onset) of 500 ms and a level of 30 dB attenuation.

The LFP responses were averaged online, and the electrodes were

positioned at the location and depth that showed the largest

evoked LFP responses over all the electrodes. Once selected, we

validated and recorded the BBN responses of the recording site

using a sequence of 280 BBN bursts with duration of 200 ms,

10 ms linear onset and offset ramps, ISI of 500 ms, and seven

different attenuation levels, between 0 and 60 dB with 10 dB steps,

that were presented pseudo-randomly so that each level was

presented 40 times. The main data were collected if noise

threshold level was at least 30 dB attenuation and noise-evoked

potentials changed regularly with level; otherwise, the electrodes

were moved to a different location.

Quasi-random frequency-level sequence of 777 tone bursts

(50 ms duration, 5 ms onset/offset linear ramps, 500 ms ISI) at 37

frequencies (1–64 kHz, 6 tones/octave) and 7 attenuation levels

(80–20 dB, 10dB steps, roughly corresponding to 20–80 dB SPL)

were used to measure the frequency response area (FRA) of the

recording site (3 presentations at each frequency-level combina-

tion). When the FRA was narrow or not smoothly graded with

level, 370 tone bursts (300 ms ISI) at 37 frequencies (1–64 kHz, or

narrower ranges when the FRA was narrow) were presented at a

fixed attenuation level, in order to better characterize the



frequency response in the level at which the main paradigm would

be presented.

Once the recording site was characterized in terms of the best

frequency and the minimum threshold, two frequencies, f1 and f2,

were selected for the main experimental paradigm. Both tone had

to be within the FRA, usually close and symmetric around the best

frequency, and to have about the same response amplitude. The

possible values of the difference between the two frequencies,

defined as: Df = f2/f121, were 10%, 21%, 44% or 96%.

We tested the responses to these frequencies in sets of up to

seven different sequences. In most cases, each sequence consisted

of 30 ms tone beeps with ISI (onset to onset) of 300 ms. A limited

amount of data were recorded using sequences with ISIs of 500,

700, 1000, 1200 and 1800 ms. Each set of sequences was

presented at a constant sound level, 20 – 40 dB above the

minimum threshold of the recording site. The sequences are

described in details in the Results section and in Figs. 1A and 3A.

Each set of the seven sequences tested the responses to f1 and f2 in

six different conditions.

In order to get comparable data from many paradigms in the

same recording site, the sequences had to be as short as possible.

Preliminary experiments showed that 25 presentations were

enough to estimate an average response with a reasonable signal

to noise ratio. Therefore, with the lowest presentation probability

of each frequency being 5%, we used sequences of 500 tone beeps.

Data AnalysisThe data were analyzed with Matlab (The Mathworks, Inc.,

Natick, MA). To analyze LFP, all the responses to each frequency

in a sequence were aligned on stimulus onset, and each was

baseline-corrected by subtracting its average during the 5 ms

interval starting at stimulus onset (response onset latency was

always longer than 8 ms, and we wanted to adjust the baseline as

close to the response onset as possible in order to avoid influence of

slow waves in the signal). Response strength was quantified by the

depth of the maximal (most negative) trough of the average

response in the interval 0–70 ms after stimulus onset. We used a

long time interval relative to stimulus duration (70 ms compared

with 30 ms) because there was a tendency for adapted responses to

be also delayed in time. The variability of the response was

quantified by the standard error of the mean (s.e.m.) of the

baseline-corrected responses at the time of the maximal trough of

the average. Only sets with clear responses and a signal to noise

ratio (response size divided by standard error) larger than 2 were

included in the analysis.

To detect MUA, the raw signals were filtered between 200 and

8000 Hz, and large, fast events were marked as spikes. The

threshold for spike detection was set to 12 times the median of the

absolute deviations from the median (MAD) of the filtered voltage

traces (corresponding to more than 7 standard deviations for

Gaussian signals). This conservative criterion ensured that the

detected events were indeed spikes and not random fluctuations of

the baseline. The resulting spike trains were aligned on stimulus

onset, smoothed with a 10 ms Hamming window, and averaged.

MUA response amplitude was quantified by the peak response in

the interval 0–70 ms after stimulus onset, without baseline

corrections.

Responses of MUA were included in the data analyzed here

only if they were clearly driven by the random-frequency tone

sequences, and had statistically significant responses to the deviant-

alone conditions in the 50 ms response interval starting at stimulus

onset, compared with the 50 ms interval of spontaneous activity

just preceding stimulus onset (2-tailed t-test, p,0.01). A few cases

with very intense but short onset bursts resulted in no net increase

of the average response rate during the 50 ms interval, but were

nevertheless included in the data. On the other hand, this criterion

rejected about 20% of the MUA data, in which responses were

present but were late (deviant-alone response latency .50 ms).

These late responses were not included in the population analysis.

Supporting Information

Text S1 The relationships between the model used in the paper

and models of synaptic depletion.

(DOC)

Author Contributions

Conceived and designed the experiments: IN NT. Performed the

experiments: NT AY. Analyzed the data: NT. Wrote the paper: NT IN.

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